{"files"=>["https://ndownloader.figshare.com/files/420527", "https://ndownloader.figshare.com/files/420637", "https://ndownloader.figshare.com/files/420669", "https://ndownloader.figshare.com/files/420699", "https://ndownloader.figshare.com/files/420772", "https://ndownloader.figshare.com/files/420862", "https://ndownloader.figshare.com/files/420950", "https://ndownloader.figshare.com/files/420980", "https://ndownloader.figshare.com/files/421020", "https://ndownloader.figshare.com/files/421081"], "description"=>"<div><p>Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.</p></div>", "links"=>[], "tags"=>["describing", "driven", "models", "neurons", "networks"], "article_id"=>143025, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000815.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s010"], "stats"=>{"downloads"=>5, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/NeuroML_A_Language_for_Describing_Data_Driven_Models_of_Neurons_and_Networks_with_a_High_Degree_of_Biological_Detail/143025", "title"=>"NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-06-17 00:50:25"}

{"files"=>["https://ndownloader.figshare.com/files/844521"], "description"=>"<p>The main element for expressing a branching neuronal structure in NeuroML is <i>cell</i> which is used for all Levels in NeuroML. The core of the cell description is a set of <i>segment</i> elements which describe the 3D shape of the cell. These can be grouped into <i>cables</i> which represent unbranched neurites of the cell. Metadata present in the cell description can contain details of the creators of the cell model, or the data on which it was based (e.g. a neuronal reconstruction from NeuroMorpho.org). Addition of the <i>biophysics</i> element allows a Level 2 conductance based spiking cell model to be described, and the <i>connectivity</i> element can be used for the allowed synaptic connectivity of a Level 3 cell (e.g. to be used when connecting the cell in a network). A detailed description of each of these elements can be found in Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s001\" target=\"_blank\">Text S1</a>. Only the elements in Level 1 which are normally used in compartmental cell modeling are shown in the figure. Other elements such as <i>freePoints</i>, <i>features</i> etc. could be present in a Level 1 file from a camera lucida reconstruction <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Crook1\" target=\"_blank\">[38]</a>.</p>", "links"=>[], "tags"=>["cells", "neuroml", "levels"], "article_id"=>514955, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Elements_for_representing_cells_in_NeuroML_Levels_1_3_/514955", "title"=>"Elements for representing cells in NeuroML Levels 1-3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:22:35"}

{"files"=>["https://ndownloader.figshare.com/files/843922"], "description"=>"<p>Level 1 incorporates MorphML, which allows descriptions of cell structure ranging from single compartment cells to detailed cells based on morphological reconstructions. Metadata describing the provenance of the data (authors, citations, etc.) can be used at this and subsequent Levels. Level 2 builds on Level 1 to specify the passive properties and the location and densities of active conductances on the cell, and includes ChannelML, for description of the membrane processes that generate the electrophysiological behavior of cells. Level 3 contains NetworkML, allowing networks of these neuronal models and their synaptic connections to be described. MorphML, ChannelML and NetworkML can be used in isolation to describe model components, while a Level X file can include any elements from that and any lower Level.</p>", "links"=>[], "tags"=>["levels", "neuroml", "channelml"], "article_id"=>514361, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_the_three_Levels_of_NeuroML_and_MorphML_ChannelML_and_NetworkML_/514361", "title"=>"Relationship between the three Levels of NeuroML and MorphML, ChannelML and NetworkML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:12:41"}